Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis

AbstractAlthough amyloid beta (Aβ) deposition is one of the major causes of white matter (WM) alterations in Alzheimer’s disease (AD), little is known about the underlying basis of WM damage and its association with global structural connectivity and network topology. We aimed to dissect the contributions of WM microstructure to structural connectivity and network properties in the ArcAβ mice model of Aβ amyloidosis.We acquired diffusion-weighted images (DWI) of wild type (WT) and ArcAβ transgenic (TG) mice using a 9.4 T MRI scanner. Fixel-based analysis (FBA) was performed to measure fiber tract-specific properties. We also performed three complementary experiments; to identify the global differences in structural connectivity, to compute network properties and to measure cellular basis of white matter alterations.Transgenic mice displayed disrupted structural connectivity centered to the entorhinal cortex (EC) and a lower fiber density and fiber bundle cross-section. In addition, there was a reduced network efficiency and degree centrality in weighted structural connectivity in the transgenic mice. To further examine the underlying neuronal basis of connectivity and network deficits, we performed histology experiments. We found no alteration in myelination and an increased level of neurofilament light (NFL) in the brain regions with disrupted connectivity in the TG mice. Furthermore, TG mice had a reduced number of perineuronal nets (PNN) in the EC.The observed FDC reductions may indicate a decrease in axonal diameter or axon count which would explain the basis of connectivity deficits and reduced network efficiency in TG mice. The increase in NFL suggests a breakdown of axonal integrity, which would reduce WM fiber health. Considering the pivotal role of the EC in AD, Aβ deposition may primarily increase NFL release, damaging PNN in the entorhinal pathway, resulting in disrupted structural connectivity.

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Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

Communications Biology ◽

10.1038/s42003-021-01700-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Arian Ashourvan ◽

Preya Shah ◽

Adam Pines ◽

Shi Gu ◽

Christopher W. Lynn ◽

...

Keyword(s):

White Matter ◽

Maximum Entropy ◽

Large Scale ◽

Structural Connectivity ◽

Quantitative Relationship ◽

Brain Regions ◽

Maximum Entropy Model ◽

Entropy Model ◽

Activation Patterns ◽

Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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Increased extracellular fluid is associated with white matter fiber degeneration in CADASIL: in vivo evidence from diffusion magnetic resonance imaging

Fluids and Barriers of the CNS ◽

10.1186/s12987-021-00264-1 ◽

2021 ◽

Vol 18 (1) ◽

Author(s):

Xinfeng Yu ◽

Xinzhen Yin ◽

Hui Hong ◽

Shuyue Wang ◽

Yeerfan Jiaerken ◽

...

Keyword(s):

White Matter ◽

Diffusion Tensor ◽

Small Vessel Disease ◽

Extracellular Fluid ◽

Free Water ◽

Inverse Correlation ◽

Brain Regions ◽

Fiber Density ◽

Four Levels

Abstract Background White matter hyperintensities (WMHs) are one of the hallmarks of cerebral small vessel disease (CSVD), but the pathological mechanisms underlying WMHs remain unclear. Recent studies suggest that extracellular fluid (ECF) is increased in brain regions with WMHs. It has been hypothesized that ECF accumulation may have detrimental effects on white matter microstructure. To test this hypothesis, we used cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) as a unique CSVD model to investigate the relationships between ECF and fiber microstructural changes in WMHs. Methods Thirty-eight CADASIL patients underwent 3.0 T MRI with multi-model sequences. Parameters of free water (FW) and apparent fiber density (AFD) obtained from diffusion-weighted imaging (b = 0 and 1000 s/mm2) were respectively used to quantify the ECF and fiber density. WMHs were split into four subregions with four levels of FW using quartiles (FWq1 to FWq4) for each participant. We analyzed the relationships between FW and AFD in each subregion of WMHs. Additionally, we tested whether FW of WMHs were associated with other accompanied CSVD imaging markers including lacunes and microbleeds. Results We found an inverse correlation between FW and AFD in WMHs. Subregions of WMHs with high-level of FW (FWq3 and FWq4) were accompanied with decreased AFD and with changes in FW-corrected diffusion tensor imaging parameters. Furthermore, FW was also independently associated with lacunes and microbleeds. Conclusions Our study demonstrated that increased ECF was associated with WM degeneration and the occurrence of lacunes and microbleeds, providing important new insights into the role of ECF in CADASIL pathology. Improving ECF drainage might become a therapeutic strategy in future.

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Asymmetric high-order anatomical brain connectivity sculpts effective connectivity

Network Neuroscience ◽

10.1162/netn_a_00150 ◽

2020 ◽

Vol 4 (3) ◽

pp. 871-890

Author(s):

Arseny A. Sokolov ◽

Peter Zeidman ◽

Adeel Razi ◽

Michael Erb ◽

Philippe Ryvlin ◽

...

Keyword(s):

White Matter ◽

Dynamic Range ◽

Diffusion Processes ◽

Brain Connectivity ◽

Effective Connectivity ◽

Structural Connectivity ◽

Laplacian Matrix ◽

Graph Laplacian ◽

Brain Regions ◽

High Order

Bridging the gap between symmetric, direct white matter brain connectivity and neural dynamics that are often asymmetric and polysynaptic may offer insights into brain architecture, but this remains an unresolved challenge in neuroscience. Here, we used the graph Laplacian matrix to simulate symmetric and asymmetric high-order diffusion processes akin to particles spreading through white matter pathways. The simulated indirect structural connectivity outperformed direct as well as absent anatomical information in sculpting effective connectivity, a measure of causal and directed brain dynamics. Crucially, an asymmetric diffusion process determined by the sensitivity of the network nodes to their afferents best predicted effective connectivity. The outcome is consistent with brain regions adapting to maintain their sensitivity to inputs within a dynamic range. Asymmetric network communication models offer a promising perspective for understanding the relationship between structural and functional brain connectomes, both in normalcy and neuropsychiatric conditions.

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Diffusion Tensor Imaging and Fiber Tractography

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications ◽

10.4018/978-1-60566-314-2.ch015 ◽

2009 ◽

pp. 229-246

Author(s):

Evanthia E. Tripoliti ◽

Dimitrios I. Fotiadis ◽

Konstantia Veliou

Keyword(s):

Diffusion Tensor Imaging ◽

White Matter ◽

Diffusion Tensor ◽

Brain Regions ◽

Cerebral White Matter ◽

Tissue Microstructure ◽

Diffusion Weighted Images ◽

Fiber Tracts ◽

Diffusion Weighted ◽

Magnetic Resonance Imaging Mri

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain structures and neural connectivity. DTI measures are thought to be representative of brain tissue microstructure and are particularly useful for examining organized brain regions, such as white matter tract areas. DTI measures the water diffusion tensor using diffusion weighted pulse sequences which are sensitive to microscopic random water motion. The resulting diffusion weighted images (DWI) display and allow quantification of how water diffuses along axes or diffusion encoding directions. This can help to measure and quantify the tissue’s orientation and structure, making it an ideal tool for examining cerebral white matter and neural fiber tracts. In this chapter the authors discuss the theoretical aspects of DTI, the information that can be extracted from DTI data, and the use of the extracted information for the reconstruction of fiber tracts and the diagnosis of a disease. In addition, a review of known fiber tracking algorithms is presented.

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Plasma neurofilament light and cerebrospinal fluid biomarkers are associated with white matter damage in Alzheimer's disease

10.21203/rs.3.rs-115308/v1 ◽

2020 ◽

Author(s):

Fardin Nabizadeh ◽

Mohammad Balabandian ◽

Mohammad Reza Rostami ◽

Samuel Berchi Kankam

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Diffusion Tensor ◽

Brain Regions ◽

Csf Biomarkers ◽

Cross Sectional ◽

Neurofilament Light ◽

Microstructural Changes ◽

Cerebrospinal Fluid Biomarkers

Abstract The most replicated blood biomarker for monitoring Alzheimer’s disease is neurofilament light (NFL). Recent evidence revealed that the plasma level of the NFL has a strong predictive value in cognitive decline and is elevated in AD patients. The Diffusion Tensor Imaging (DTI) is understood to reflect white matter disruption, neurodegeneration largely, and synaptic damage in AD. However, there is no investigation of the association between plasma NFL and white matter microstructure integrity. we have investigated the cross-sectional associations of plasma NFL, CSF tau, p tau, and Aβ with white matter microstructural changes as measured by DTI in 92 mild cognitive impairment (MCI) participants. We investigated potential correlations of the DTI values of each region of the MNI atlas, with plasma NFL, CSF total tau, CSF p tau, and as well as CSF Aβ, separately using a partial correlation model controlled for the effect of age, sex and APOE ε4 genotype. Our findings revealed a significant correlation between plasma and CSF biomarkers with altered white matter microstructural changes in widespread brain regions. Plasma NFL has a negative correlation with FA and positive correlation with RD, AD, and MD values in different regions. Plasma NFL promises to be an early biomarker of microstructural changes in MCI and for MCI progression to AD.

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The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

Network Neuroscience ◽

10.1162/netn_a_00179 ◽

2020 ◽

pp. 1-15

Author(s):

Tommy Boshkovski ◽

Ljupco Kocarev ◽

Julien Cohen-Adad ◽

Bratislav Mišić ◽

Stéphane Lehéricy ◽

...

Keyword(s):

White Matter ◽

Diffusion Mri ◽

Brain Connectivity ◽

Structural Connectivity ◽

Brain Regions ◽

Added Value ◽

Modular Organization ◽

Sensitive Measure ◽

Higher Order Functions ◽

Insight Into

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

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Using structural connectivity to augment community structure in EEG functional connectivity

Network Neuroscience ◽

10.1162/netn_a_00147 ◽

2020 ◽

Vol 4 (3) ◽

pp. 761-787 ◽

Cited By ~ 3

Author(s):

Katharina Glomb ◽

Emeline Mullier ◽

Margherita Carboni ◽

Maria Rubega ◽

Giannarita Iannotti ◽

...

Keyword(s):

Community Structure ◽

White Matter ◽

Functional Connectivity ◽

Large Scale ◽

Structural Connectivity ◽

Brain Regions ◽

Source Analysis ◽

Volume Conduction ◽

Functional Relationships ◽

The Impact

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

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Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

10.1101/2021.12.14.21267815 ◽

2021 ◽

Author(s):

Ajay Peddada ◽

Kevin Holly ◽

Tejaswi D Sudhakar ◽

Christina Ledbetter ◽

Christopher E. Talbot ◽

...

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Temporal Lobe ◽

Resting State ◽

Classification Accuracy ◽

Structural Connectivity ◽

Brain Regions ◽

Group Differences ◽

Functional Connections ◽

White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

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Plasma neurofilament light is associated with white matter damage in Alzheimer's disease

10.21203/rs.3.rs-115308/v2 ◽

2020 ◽

Author(s):

Fardin Nabizadeh ◽

Mohammad Balabandian ◽

Mohammad Reza Rostami ◽

Samuel Berchi Kankam ◽

Fetemeh Ranjbaran ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

White Matter ◽

Diffusion Tensor ◽

Amyloid Β ◽

Brain Regions ◽

Csf Biomarkers ◽

Cross Sectional ◽

Neurofilament Light ◽

Microstructural Changes

Abstract The most replicated blood biomarker for monitoring Alzheimer’s disease is neurofilament light (NFL). Recent evidence revealed that the plasma level of the NFL has a strong predictive value in cognitive decline and is elevated in AD patients. The Diffusion Tensor Imaging (DTI) is understood to reflect white matter disruption, neurodegeneration, and synaptic damage in AD. However, few investigations have been carried out on the association between plasma NFL and white matter microstructure integrity. We have investigated the cross-sectional associations of plasma NFL, CSF total tau, phosphorylated tau, and Amyloid β with white matter microstructural changes as measured by DTI in 92 mild cognitive impairment (MCI) participants. We investigated potential correlations of the DTI values of each region of the MNI atlas, with plasma NFL, separately using a partial correlation model controlled for the effect of age, sex, and APOE ε4 genotype. Our findings revealed a significant correlation between plasma and CSF biomarkers with altered white matter microstructural changes in widespread brain regions. Plasma NFL negatively correlates with FA and the positive correlation with RD, DA, and MD values in different regions. Our findings showed that plasma NFL is associated with white matter changes and AD-related features, including atrophy and hypometabolism. Plasma NFL promises to be an early biomarker of microstructural changes in MCI and MCI progression to AD.

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Behavioral Effects of Chronic Gray and White Matter Stroke Lesions in a Functionally Defined Connectome for Naming

Neurorehabilitation and Neural Repair ◽

10.1177/1545968318780351 ◽

2018 ◽

Vol 32 (6-7) ◽

pp. 613-623 ◽

Cited By ~ 4

Author(s):

Shihui Xing ◽

Ayan Mandal ◽

Elizabeth H. Lacey ◽

Laura M. Skipper-Kallal ◽

Jinsheng Zeng ◽

...

Keyword(s):

White Matter ◽

Left Hemisphere ◽

Phonological Processing ◽

Semantic Processing ◽

Structural Connectivity ◽

Dorsal Stream ◽

Naming Task ◽

Brain Regions ◽

Lesion Size ◽

The Impact

Background. In functional magnetic resonance imaging studies, picture naming engages widely distributed brain regions in the parietal, frontal, and temporal cortices. However, it remains unknown whether those activated areas, along with white matter pathways between them, are actually crucial for naming. Objective. We aimed to identify nodes and pathways implicated in naming in healthy older adults and test the impact of lesions to the connectome on naming ability. Methods. We first identified 24 cortical nodes activated by a naming task and reconstructed anatomical connections between these nodes using probabilistic tractography in healthy adults. We then used structural scans and fractional anisotropy (FA) maps in 45 patients with left hemisphere stroke to assess the relationships of node and pathway integrity to naming, phonology, and nonverbal semantic ability. Results. We found that mean FA values in 13 left hemisphere white matter tracts within the dorsal and ventral streams and 1 interhemispheric tract significantly related to naming scores after controlling for lesion size and demographic factors. In contrast, lesion loads in the cortical nodes were not related to naming performance after controlling for the same variables. Among the identified tracts, the integrity of 4 left hemisphere ventral stream tracts related to nonverbal semantic processing and 1 left hemisphere dorsal stream tract related to phonological processing. Conclusions. Our findings reveal white matter structures vital for naming and its subprocesses. These findings demonstrate the value of multimodal methods that integrate functional imaging, structural connectivity, and lesion data to understand relationships between brain networks and behavior.

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